Transcriptomic analyses and experimental verification reveal potential biomarkers and biological pathways of urinary tract infection

Decoding Immune Dynamics in Pregnant Women: Key Gene Expression Changes Following Influenza Vaccination

Pregnant women are at an increased risk of severe influenza complications, necessitating vaccination as a preventive measure. Despite World Health Organization (WHO) recommendations for influenza vaccination during pregnancy, vaccination rates remain suboptimal in many regions. This study aims to identify key differentially expressed genes (DEGs) and biological pathways modulated by influenza vaccination in pregnant women pre- and post-vaccination, contributing to improved vaccine strategies. Microarray data from gene expression omnibus GEO dataset GSE166545 was analyzed to identify DEGs in blood samples from pregnant women at three time points: pre-vaccination (Day 0) and post-vaccination (Days 0 and 1) (Days 1 and 7). DEGs were filtered using an adjusted p-value < 0.05 and |log2 fold change| ≥ 1. Protein/protein interaction (PPI) networks, hub gene identification, and pathway enrichment analyses were conducted using STRING, Cytoscape, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome databases. Hub gene validation was performed using the Human Protein Atlas (HPA) and GTEx Portal. The GSE166545 dataset analysis revealed 60 up-regulated and 12,854 down-regulated genes (Day 1 vs. 7), 55 up-regulated and 12,933 down-regulated genes (Day 0 vs. 1), and two up-regulated with no down-regulated genes (Day 0 vs. 7). Key pathways included interferon alpha/beta (IFN-γ\ β) signaling and toll-like receptor signaling (TLR). Hub genes such as GBP1, CXCL10, RSAD2, and IFI44 demonstrated robust up-regulation, correlating with enhanced immune responses. The initial observation of JCHAIN's notable up-regulation occurred on the seventh day following vaccination. Validation confirmed these genes' roles in antiviral defense mechanisms and vaccine responses. The findings reveal distinct immune response dynamics in pregnant women following influenza vaccination, highlighting potential biomarkers for vaccine efficacy. This study underscores the importance of tailored vaccine strategies to improve maternal and neonatal outcomes.

Application of metagenomic next-generation sequencing in the diagnosis of urinary tract infection in patients undergoing cutaneous ureterostomy

Objective

Urinary tract infection (UTI) is an inflammatory response of the urothelium to bacterial invasion and is a common complication in patients with cutaneous ureterostomy (CU). For such patients, accurate and efficient identification of pathogens remains a challenge. The aim of this study included exploring utility of metagenomic next-generation sequencing (mNGS) in assisting microbiological diagnosis of UTI among patients undergoing CU, identifying promising cytokine or microorganism biomarkers, revealing microbiome diversity change and compare virulence factors (VFs) and antibiotic resistance genes (ARGs) after infection.

Methods

We performed a case-control study of 50 consecutive CU patients from December 2020 to January 2021. According to the clinical diagnostic criteria, samples were divided into infected group and uninfected group and difference of urine culture, cytokines, microorganism, ARGs and VFs were compared between the two groups.

Results

Inflammatory responses were more serious in infected group, as evidenced by a significant increase in IFN-α (p=0.031), IL-1β (0.023) and IL-6 (p=0.018). Clinical culture shows that there is higher positive rate in infected group for most clinical pathogens like Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Candida auris etc. and the top three pathogens with positive frequencies were E. coli, K. pneumoniae, and Enterococcus faecalis. Benchmarking clinical culture, the total sensitivity is 91.4% and specificity is 76.3% for mNGS. As for mNGS, there was no significant difference in microbiome α- diversity between infected and uninfected group. Three species biomarkers including Citrobacter freundii, Klebsiella oxytoca, and Enterobacter cloacae are enriched in infected group based on Lefse. E. cloacae were significantly correlated with IL-6 and IL-10. K. oxytoca were significantly correlated with IL-1β. Besides, the unweighted gene number and weighted gene abundance of VFs or ARGs are significantly higher in infected group. Notablely, ARGs belonging to fluoroquinolones, betalatmas, fosfomycin, phenicol, phenolic compound abundance is significantly higher in infected group which may have bad effect on clinical treatment for patients.

Conclusion

mNGS, along with urine culture, will provide comprehensive and efficient reference for the diagnosis of UTI in patients with CU and allow us to monitor microbial changes in urine of these patients. Moreover, cytokines (IL-6, IL-1β, and IFN-a) or microorganisms like C. freundii, K. oxytoca or E. cloacae are promising biomarkers for building effective UTI diagnostic model of patients with CU and seriously the VFs and ARGs abundance increase in infected group may play bad effect on clinical treatment.

Screening Biomarkers and Constructing a Predictive Model for Symptomatic Urinary Tract Infection and Asymptomatic Bacteriuria in Patients Undergoing Cutaneous Ureterostomy: A Metagenomic Next-Generation Sequencing Study

Objectives. To investigate the clinical diagnostic value of differential flora as biomarkers in patients with symptomatic urinary tract infection (UTI) and asymptomatic bacteriuria (ASB) undergoing cutaneous ureterostomy based on metagenomic next-generation sequencing and construct predictive models to provide a scientific reference for clinical diagnosis and treatment. Material and Methods. According to standard procedures, samples were taken from each patient for routine tests (urine, ureteral stent, and skin swab around the stoma). Cytokine levels in the blood were also detected. Urinary microflora were measured by mNGS, and potential biomarkers for distinguishing UTI and ASB were identified by differential flora. Finally, we generated the predictive models for ASB and UTI using the Lasso method and cytokine levels. Results. Urine culture was performed for 50 patients with cutaneous ureterostomy; 44 of these patients developed bacteriuria. The incidence of symptomatic bacteriuria was 54.55%. Biomarker analysis showed that Propionimicrobium lymphophilum, Staphylococcus haemolyticus, Stenotrophomonas maltophilia, Ralstonia insidiosa, and Aspergillus sydowii all had good predictive performance and were combined in a single model. The predictive model exhibited good prediction performance (area under the curve $\left(\text{AUC}\right)=0.8729$ , $\text{sensitivity}=80\%$ , $\text{specificity}=83.3\%$ , and $\text{cutoff}=1.855$ ). We also identified a significant negative correlation between the weight sum of the abundance for these five characteristic pathogens (Sum_weighted_Reads) and levels of the cytokine IL-6 and IL-1β ( $P<0.05$ ). Conclusion. mNGS had a higher positive detection rate for pathogens in urine samples. The selected differential bacteria can be used as biomarkers of ASB and UTI, and the prediction model has good predictive performance. Analysis also showed that the occurrence of symptoms was related to individual immunity. Combined with the Sum_weighted_Reads cutoff and cytokine levels (IL-6 and IL-1β) of differential flora, it was possible to judge the severity of symptoms in cutaneous ureterostomy patients with bacteriuria and provide new insights for the treatment and intervention of ASB and UTI.